Accountants, auditors, and scientists are frequently faced with the task of locating anomalies in sets of data. Accountants and auditors, for instance, may be interested in detecting fraud in financial data. To a scientist, an anomaly in a set of data may indicate an interesting property of the system being studied or a systemic measuring error.
One existing technique for detecting anomalies in a set of data is based on Benford's law. Benford's law describes the rate at which the first few digits in a list of numbers from many real-life sources of data is expected to occur. For example, according to Benford's law, a one (“1”) digit should occur in the leading digit almost one-third of the time while the digit nine (“9”) should occur as a leading digit less than one time in twenty. Anomalies can be detected using Benford's law by looking at the actual distribution of first digits in a set of data and comparing it to the distribution expected based on Benford's law.